Energy-Efficient Long-term Continuous Personal Health Monitoring

Abstract

Continuous health monitoring using wireless body area networks of implantable and wearable medical devices (IWMDs) is envisioned as a transformative approach to healthcare. Rapid advances in biomedical sensors, low-power electronics, and wireless communications have brought this vision to the verge of reality. However, key challenges still remain to be addressed. The constrained sizes of IWMDs imply that they are designed with very limited processing, storage, and battery capacities. Therefore, there is a very strong need for efficiency in data collection, analysis, storage, and communication. In this paper, we first quantify the energy and storage requirements of a continuous personal health monitoring system that uses eight biomedical sensors: (1) heart rate, (2) blood pressure, (3) oxygen saturation, (4) body temperature, (5) blood glucose, (6) accelerometer, (7) electrocardiogram (ECG), and (8) electroencephalogram (EEG). Our analysis suggests that there exists a significant gap between the energy and storage requirements for long-term continuous monitoring and the capabilities of current devices. To enable energy-efficient continuous health monitoring, we propose schemes for sample aggregation, anomaly-driven transmission, and compressive sensing to reduce the overheads of wirelessly transmitting, storing, and encrypting/authenticating the data. We evaluate these techniques and demonstrate that they result in two to three orders-of-magnitude improvements in energy and storage requirements, and can help realize the potential of long-term continuous health monitoring.